code: def _execute_and_compute_jvp(
      tapes, execute_fn, jpc, device, primals, tangents
  ):
      """
      Compute the results and jvps using a pure callback around
      :meth:`~.JacobianProductCalculator.execute_and_compute_jacobian`.
  
      Note that we must query the full jacobian inside the pure-callback so that jax can trace the JVP
      calculation.
      """
      # Select the trainable params. Non-trainable params contribute a 0 gradient.
      for tangent, tape in zip(tangents[0], tapes.vals):
          tape.trainable_params = tuple(
              idx
              for idx, t in enumerate(tangent)
              if not isinstance(t, Zero)
          )
      tangents_trainable = tuple(
          tuple(t for t in tangent if not isinstance(t, Zero))
          for tangent in tangents[0]
      )
  
      def wrapper(inner_params):
          new_tapes = _set_all_parameters_on_copy(
              tapes.vals, inner_params
          )
          return _to_jax(jpc.execute_and_compute_jacobian(new_tapes))
  
      res_struct = tuple(
          _result_shape_dtype_struct(t, device) for t in tapes.vals
      )
      jac_struct = tuple(
          _jac_shape_dtype_struct(t, device) for t in tapes.vals
      )
      results, jacobians = jax.pure_callback(
          wrapper, (res_struct, jac_struct), primals[0]
      )
  
      jvps = _compute_jvps(jacobians, tangents_trainable, tapes.vals)
  
      return results, jvps
  
file_path: pennylane/pennylane/workflow/interfaces/jax_jit.py
imports: import jax
  import jax.numpy
  import pennylane
  from pennylane.typing import ResultBatch
  from ..jacobian_products import _compute_jvps
  from .jax import _NonPytreeWrapper
label_desc: Use image node_id 9 for calling a global function with example usage: _execute_and_compute_jvp(tapes, execute_fn, jpc, device, primals, tangents) and returns: results,  jvps, _to_jax